Post-hoc approaches rescale the confidence scores of an uncalibrated neural network to make it calibrated. The scaling hyper-parameters are chosen on a held-out validation set. Some of the recently proposed approaches are temperature scaling [1]}, scaling and binning calibration [2]}, Dirichlet calibration [3]}, and beta calibration [4]}. These approaches find motivation from classical methods such as Platt scaling [5]}, binning [6]}, and isotonic regression [7]}.
